SummaryParkinson’s disease (PD) is the second most common neurodegenerative disorder primarily caused by the progressive loss of dopaminergic (DA) neurons in the substantia nigra (SN). Despite the advances in gene discovery associated with PD, the knowledge of the PD pathogenesis is largely limited to the involvement of these genes in the generic cell death pathways, and why degeneration is specific to DA neurons and why the degeneration is progressive remain enigmatic. Broad goal of our work is therefore to elucidate the mechanisms underlying specific and progressive DA neuron degeneration in PD. Our new Drosophila model of PD ⎯Fer2 gene loss-of-function mutation⎯ is unusually well suited to address these questions. Fer2 mutants exhibit specific and progressive death of brain DA neurons as well as severe locomotor defects and short life span. Strikingly, the death of DA neuron is initiated in a small cluster of Fer2-expressing DA neurons and subsequently propagates to Fer2-negative DA neurons. We therefore propose a novel two-step model of the neurodegeneration in PD: primary cell death occurs in a specific subset of dopamindegic neurons that are genetically defined, and subsequently the failure of the neuronal connectivity triggers and propagates secondary cell death to remaining DA neurons. In this research, we will test this hypothesis and investigate the underlying molecular mechanisms. This will be the first study to examine circuit-dependency in DA neuron degeneration. Our approach will use a combination of non-biased genomic techniques and candidate-based screening, in addition to the powerful Drosophila genetic toolbox. Furthermore, to test this hypothesis beyond the Drosophila model, we will establish new mouse models of PD that exhibit progressive DA neuron degeneration. Outcome of this research will likely revolutionize the understanding of PD pathogenesis and open an avenue toward the discovery of effective therapy strategies against PD.

Parkinson’s disease (PD) is the second most common neurodegenerative disorder primarily caused by the progressive loss of dopaminergic (DA) neurons in the substantia nigra (SN). Despite the advances in gene discovery associated with PD, the knowledge of the PD pathogenesis is largely limited to the involvement of these genes in the generic cell death pathways, and why degeneration is specific to DA neurons and why the degeneration is progressive remain enigmatic. Broad goal of our work is therefore to elucidate the mechanisms underlying specific and progressive DA neuron degeneration in PD. Our new Drosophila model of PD ⎯Fer2 gene loss-of-function mutation⎯ is unusually well suited to address these questions. Fer2 mutants exhibit specific and progressive death of brain DA neurons as well as severe locomotor defects and short life span. Strikingly, the death of DA neuron is initiated in a small cluster of Fer2-expressing DA neurons and subsequently propagates to Fer2-negative DA neurons. We therefore propose a novel two-step model of the neurodegeneration in PD: primary cell death occurs in a specific subset of dopamindegic neurons that are genetically defined, and subsequently the failure of the neuronal connectivity triggers and propagates secondary cell death to remaining DA neurons. In this research, we will test this hypothesis and investigate the underlying molecular mechanisms. This will be the first study to examine circuit-dependency in DA neuron degeneration. Our approach will use a combination of non-biased genomic techniques and candidate-based screening, in addition to the powerful Drosophila genetic toolbox. Furthermore, to test this hypothesis beyond the Drosophila model, we will establish new mouse models of PD that exhibit progressive DA neuron degeneration. Outcome of this research will likely revolutionize the understanding of PD pathogenesis and open an avenue toward the discovery of effective therapy strategies against PD.

SummaryOur affective and motivational state is important for our decisions, actions and quality of life. Many pathological conditions affect this state. For example, addictive drugs are hyperactivating the reward system and trigger a strong motivation for continued drug intake, whereas many somatic and psychiatric diseases lead to an aversive state, characterized by loss of motivation. I will study specific neural circuits and mechanisms underlying reward and aversion, and how pathological signaling in these systems can trigger relapse in drug addiction.
Given the important role of the dopaminergic neurons in the midbrain for many aspects of reward signaling, I will study how synaptic plasticity in these cells, and in their target neurons in the striatum, contribute to relapse in drug seeking. I will also study the circuits underlying aversion. Little is known about these circuits, but my hypothesis is that an important component of aversion is signaled by a specific neuronal population in the brainstem parabrachial nucleus, projecting to the central amygdala. We will test this hypothesis and also determine how this aversion circuit contributes to the persistence of addiction and to relapse.
To dissect this complicated system, I am developing new genetic methods for manipulating and visualizing specific functional circuits in the mouse brain. My unique combination of state-of-the-art competence in transgenics and cutting edge knowledge in the anatomy and functional organization of the circuits behind reward and aversion should allow me to decode these systems, linking discrete circuits to behavior.
Collectively, the results will indicate how signals encoding aversion and reward are integrated to control addictive behavior and they may identify novel avenues for treatment of drug addiction as well as aversion-related symptoms affecting patients with chronic inflammatory conditions and cancer.

Our affective and motivational state is important for our decisions, actions and quality of life. Many pathological conditions affect this state. For example, addictive drugs are hyperactivating the reward system and trigger a strong motivation for continued drug intake, whereas many somatic and psychiatric diseases lead to an aversive state, characterized by loss of motivation. I will study specific neural circuits and mechanisms underlying reward and aversion, and how pathological signaling in these systems can trigger relapse in drug addiction.
Given the important role of the dopaminergic neurons in the midbrain for many aspects of reward signaling, I will study how synaptic plasticity in these cells, and in their target neurons in the striatum, contribute to relapse in drug seeking. I will also study the circuits underlying aversion. Little is known about these circuits, but my hypothesis is that an important component of aversion is signaled by a specific neuronal population in the brainstem parabrachial nucleus, projecting to the central amygdala. We will test this hypothesis and also determine how this aversion circuit contributes to the persistence of addiction and to relapse.
To dissect this complicated system, I am developing new genetic methods for manipulating and visualizing specific functional circuits in the mouse brain. My unique combination of state-of-the-art competence in transgenics and cutting edge knowledge in the anatomy and functional organization of the circuits behind reward and aversion should allow me to decode these systems, linking discrete circuits to behavior.
Collectively, the results will indicate how signals encoding aversion and reward are integrated to control addictive behavior and they may identify novel avenues for treatment of drug addiction as well as aversion-related symptoms affecting patients with chronic inflammatory conditions and cancer.

SummaryThe project outlined here addresses the fundamental question how the brain encodes and controls behavior. While we have a reasonable understanding of the role of entire brain areas in such processes, and of mechanisms at the molecular and synaptic levels, there is a big gap in our knowledge of how behavior is controlled at the level of defined neuronal circuits.
In natural environments, chances for survival depend on learning about possible aversive and appetitive outcomes and on the appropriate behavioral responses. Most studies addressing the underlying mechanisms at the level of neuronal circuits have focused on aversive learning, such as in Pavlovian fear conditioning. Understanding how activity in defined neuronal circuits mediates appetitive learning, as well as how these circuitries are shared and interact with aversive learning circuits, is a central question in the neuroscience of learning and memory and the focus of this grant application.
Using a multidisciplinary approach in mice, combining behavioral, in vivo and in vitro electrophysiological, imaging, optogenetic and state-of-the-art viral circuit tracing techniques, we aim at dissecting the neuronal circuitry of appetitive Pavlovian conditioning with a focus on the amygdala, a key brain region important for both aversive and appetitive learning. Ultimately, elucidating these mechanisms at the level of defined neurons and circuits is fundamental not only for an understanding of memory processes in the brain in general, but also to inform a mechanistic approach to psychiatric conditions associated with amygdala dysfunction and dysregulated emotional responses including anxiety and mood disorders.

The project outlined here addresses the fundamental question how the brain encodes and controls behavior. While we have a reasonable understanding of the role of entire brain areas in such processes, and of mechanisms at the molecular and synaptic levels, there is a big gap in our knowledge of how behavior is controlled at the level of defined neuronal circuits.
In natural environments, chances for survival depend on learning about possible aversive and appetitive outcomes and on the appropriate behavioral responses. Most studies addressing the underlying mechanisms at the level of neuronal circuits have focused on aversive learning, such as in Pavlovian fear conditioning. Understanding how activity in defined neuronal circuits mediates appetitive learning, as well as how these circuitries are shared and interact with aversive learning circuits, is a central question in the neuroscience of learning and memory and the focus of this grant application.
Using a multidisciplinary approach in mice, combining behavioral, in vivo and in vitro electrophysiological, imaging, optogenetic and state-of-the-art viral circuit tracing techniques, we aim at dissecting the neuronal circuitry of appetitive Pavlovian conditioning with a focus on the amygdala, a key brain region important for both aversive and appetitive learning. Ultimately, elucidating these mechanisms at the level of defined neurons and circuits is fundamental not only for an understanding of memory processes in the brain in general, but also to inform a mechanistic approach to psychiatric conditions associated with amygdala dysfunction and dysregulated emotional responses including anxiety and mood disorders.

Max ERC Funding

2 497 200 €

Duration

Start date: 2016-01-01, End date: 2020-12-31

Project acronymastromnesis

ProjectThe language of astrocytes: multilevel analysis to understand astrocyte communication and its role in memory-related brain operations and in cognitive behavior

Researcher (PI)Andrea Volterra

Host Institution (HI)UNIVERSITE DE LAUSANNE

Call DetailsAdvanced Grant (AdG), LS5, ERC-2013-ADG

SummaryIn the 90s, two landmark observations brought to a paradigm shift about the role of astrocytes in brain function: 1) astrocytes respond to signals coming from other cells with transient Ca2+ elevations; 2) Ca2+ transients in astrocytes trigger release of neuroactive and vasoactive agents. Since then, many modulatory astrocytic actions and mechanisms were described, forming a complex - partly contradictory - picture, in which the exact roles and modes of astrocyte action remain ill defined. Our project wants to bring light into the “language of astrocytes”, i.e. into how they communicate with neurons and, ultimately, address their role in brain computations and cognitive behavior. To this end we will perform 4 complementary levels of analysis using highly innovative methodologies in order to obtain unprecedented results. We will study: 1) the subcellular organization of astrocytes underlying local microdomain communications by use of correlative light-electron microscopy; 2) the way individual astrocytes integrate inputs and control synaptic ensembles using 3D two-photon imaging, genetically-encoded Ca2+ indicators, optogenetics and electrophysiology; 3) the contribution of astrocyte ensembles to behavior-relevant circuit operations using miniaturized microscopes capturing neuronal/astrocytic population dynamics in freely-moving mice during memory tests; 4) the contribution of astrocytic signalling mechanisms to cognitive behavior using a set of new mouse lines with conditional, astrocyte-specific genetic modification of signalling pathways. We expect that this combination of groundbreaking ideas, innovative technologies and multilevel analysis makes our project highly attractive to the neuroscience community at large, bridging aspects of molecular, cellular, systems and behavioral neuroscience, with the goal of leading from a provocative hypothesis to the conclusive demonstration of whether and how “the language of astrocytes” participates in memory and cognition.

In the 90s, two landmark observations brought to a paradigm shift about the role of astrocytes in brain function: 1) astrocytes respond to signals coming from other cells with transient Ca2+ elevations; 2) Ca2+ transients in astrocytes trigger release of neuroactive and vasoactive agents. Since then, many modulatory astrocytic actions and mechanisms were described, forming a complex - partly contradictory - picture, in which the exact roles and modes of astrocyte action remain ill defined. Our project wants to bring light into the “language of astrocytes”, i.e. into how they communicate with neurons and, ultimately, address their role in brain computations and cognitive behavior. To this end we will perform 4 complementary levels of analysis using highly innovative methodologies in order to obtain unprecedented results. We will study: 1) the subcellular organization of astrocytes underlying local microdomain communications by use of correlative light-electron microscopy; 2) the way individual astrocytes integrate inputs and control synaptic ensembles using 3D two-photon imaging, genetically-encoded Ca2+ indicators, optogenetics and electrophysiology; 3) the contribution of astrocyte ensembles to behavior-relevant circuit operations using miniaturized microscopes capturing neuronal/astrocytic population dynamics in freely-moving mice during memory tests; 4) the contribution of astrocytic signalling mechanisms to cognitive behavior using a set of new mouse lines with conditional, astrocyte-specific genetic modification of signalling pathways. We expect that this combination of groundbreaking ideas, innovative technologies and multilevel analysis makes our project highly attractive to the neuroscience community at large, bridging aspects of molecular, cellular, systems and behavioral neuroscience, with the goal of leading from a provocative hypothesis to the conclusive demonstration of whether and how “the language of astrocytes” participates in memory and cognition.

SummaryLearning and memory are the basis of our behaviour and mental well-being. Understanding the mechanisms of structural and cellular plasticity in defined neuronal circuits in vivo will be crucial to elucidate principles of circuit-specific memory formation and their relation to changes in neuronal ensemble dynamics.
Structural plasticity studies were technically limited to cortex, excluding deep brain areas like the amygdala, and mainly focussed on the input site (dendritic spines), whilst the plasticity of the axon initial segment (AIS), a neuron’s site of output generation, was so far not studied in vivo. Length and location of the AIS are plastic and strongly affects a neurons spike output. However, it remains unknown if AIS plasticity regulates neuronal activity upon learning in vivo.
We will combine viral expression of AIS live markers and genetically-encoded Ca2+-sensors with novel deep brain imaging techniques via gradient index (GRIN) lenses to investigate how AIS location and length are regulated upon associative learning in amygdala circuits in vivo. Two-photon time-lapse imaging of the AIS of amygdala neurons upon fear conditioning will help us to track learning-driven AIS location dynamics. Next, we will combine miniature microscope imaging of neuronal activity in freely moving animals with two-photon imaging to link AIS location, length and plasticity to the intrinsic activity as well as learning-related response plasticity of amygdala neurons during fear learning and extinction in vivo. Finally, we will test if AIS plasticity is a general cellular plasticity mechanisms in brain areas afferent to the amygdala, e.g. thalamus.
Using a combination of two-photon and miniature microscopy imaging to map structural dynamics of defined neural circuits in the amygdala and its thalamic input areas will provide fundamental insights into the cellular mechanisms underlying sensory processing upon learning and relate network level plasticity with the cellular level.

Learning and memory are the basis of our behaviour and mental well-being. Understanding the mechanisms of structural and cellular plasticity in defined neuronal circuits in vivo will be crucial to elucidate principles of circuit-specific memory formation and their relation to changes in neuronal ensemble dynamics.
Structural plasticity studies were technically limited to cortex, excluding deep brain areas like the amygdala, and mainly focussed on the input site (dendritic spines), whilst the plasticity of the axon initial segment (AIS), a neuron’s site of output generation, was so far not studied in vivo. Length and location of the AIS are plastic and strongly affects a neurons spike output. However, it remains unknown if AIS plasticity regulates neuronal activity upon learning in vivo.
We will combine viral expression of AIS live markers and genetically-encoded Ca2+-sensors with novel deep brain imaging techniques via gradient index (GRIN) lenses to investigate how AIS location and length are regulated upon associative learning in amygdala circuits in vivo. Two-photon time-lapse imaging of the AIS of amygdala neurons upon fear conditioning will help us to track learning-driven AIS location dynamics. Next, we will combine miniature microscope imaging of neuronal activity in freely moving animals with two-photon imaging to link AIS location, length and plasticity to the intrinsic activity as well as learning-related response plasticity of amygdala neurons during fear learning and extinction in vivo. Finally, we will test if AIS plasticity is a general cellular plasticity mechanisms in brain areas afferent to the amygdala, e.g. thalamus.
Using a combination of two-photon and miniature microscopy imaging to map structural dynamics of defined neural circuits in the amygdala and its thalamic input areas will provide fundamental insights into the cellular mechanisms underlying sensory processing upon learning and relate network level plasticity with the cellular level.

Max ERC Funding

1 475 475 €

Duration

Start date: 2018-12-01, End date: 2023-11-30

Project acronymBRAINCOMPATH

ProjectMesoscale Brain Dynamics: Computing with Neuronal Pathways

Researcher (PI)Fritjof Helmchen

Host Institution (HI)UNIVERSITAT ZURICH

Call DetailsAdvanced Grant (AdG), LS5, ERC-2014-ADG

SummaryBrain computations rely on proper signal flow through the complex network of connected brain regions. Despite a wealth of anatomical and functional data – from microscopic to macroscopic scale – we still poorly understand the principles of how signal flow is routed through neuronal networks to generate appropriate behavior. Brain dynamics on the 'mesoscopic' scale, the intermediate level where local microcircuits communicate via axonal pathways, has remained a particular blind spot of research as it has been difficult to access under in vivo conditions. Here, I propose to tackle the mesoscopic level of brain dynamics both experimentally and theoretically, adopting a fresh perspective centered on neuronal pathway dynamics. Experimentally, we will utilize and further advance state-of-the-art genetic and optical techniques to create a toolbox for measuring and manipulating signal flow in pathway networks across a broad range of temporal scales. In particular, we will improve fiber-optic based methods for probing the activity of either individual or multiple neuronal pathways with high specificity. Using these tools we will set out to reveal mesoscopic brain dynamics across relevant cortical and subcortical regions in awake, behaving mice. Specifically, we will investigate sensorimotor learning for a reward-based texture discrimination task and rapid sensorimotor control during skilled locomotion. Moreover, by combining fiber-optic methods with two-photon microscopy and fMRI, respectively, we will start linking the meso-level to the micro- and macro-levels. Throughout the project, experiments will be complemented by computational approaches to analyse data, model pathway dynamics, and conceptualize a formal theory of mesoscopic dynamics. This project may transform the field by bridging the hierarchical brain levels and opening significant new avenues to assess physiological as well as pathological signal flow in the brain.

Brain computations rely on proper signal flow through the complex network of connected brain regions. Despite a wealth of anatomical and functional data – from microscopic to macroscopic scale – we still poorly understand the principles of how signal flow is routed through neuronal networks to generate appropriate behavior. Brain dynamics on the 'mesoscopic' scale, the intermediate level where local microcircuits communicate via axonal pathways, has remained a particular blind spot of research as it has been difficult to access under in vivo conditions. Here, I propose to tackle the mesoscopic level of brain dynamics both experimentally and theoretically, adopting a fresh perspective centered on neuronal pathway dynamics. Experimentally, we will utilize and further advance state-of-the-art genetic and optical techniques to create a toolbox for measuring and manipulating signal flow in pathway networks across a broad range of temporal scales. In particular, we will improve fiber-optic based methods for probing the activity of either individual or multiple neuronal pathways with high specificity. Using these tools we will set out to reveal mesoscopic brain dynamics across relevant cortical and subcortical regions in awake, behaving mice. Specifically, we will investigate sensorimotor learning for a reward-based texture discrimination task and rapid sensorimotor control during skilled locomotion. Moreover, by combining fiber-optic methods with two-photon microscopy and fMRI, respectively, we will start linking the meso-level to the micro- and macro-levels. Throughout the project, experiments will be complemented by computational approaches to analyse data, model pathway dynamics, and conceptualize a formal theory of mesoscopic dynamics. This project may transform the field by bridging the hierarchical brain levels and opening significant new avenues to assess physiological as well as pathological signal flow in the brain.

SummaryThe core function of all brains is to compute the current state of the world, compare it to the desired state of the world and select motor programs that drive behavior minimizing any mismatch. The circuits underlying these functions are the key to understand brains in general, but so far they are completely unknown. Three problems have hindered progress: 1) The animal’s desired state of the world is rarely known. 2) Most studies in simple models have focused on sensory driven, reflex-like processes, and not considered self-initiated behavior. 3) The circuits underlying complex behaviors in vertebrates are widely distributed, containing millions of neurons. With this proposal I aim at overcoming these problems using insects, whose tiny brains solve the same basic problems as our brains but with 100,000 times fewer cells. Moreover, the central complex, a single conserved brain region consisting of only a few thousand neurons, is crucial for sensory integration, motor control and state-dependent modulation, essentially being a ‘brain in the brain’. To simplify the problem further I will focus on navigation behavior. Here, the desired and actual states of the world are equal to the desired and current headings of the animal, with mismatches resulting in compensatory steering. I have previously shown how the central complex encodes the animal’s current heading. Now I will use behavioral training to generate animals with highly defined desired headings, and correlate neural activity with the animal’s ‘intentions’ and actions - at the level of identified neurons. To establish the involved conserved core circuitry valid across insects I will compare species with distinct lifestyles. Secondly, I will reveal how these circuits have evolved to account for each species’ unique ecology. The proposed work will provide a coherent framework to study key concepts of fundamental brain functions in unprecedented detail - using a single, conserved, but flexible neural circuit.

The core function of all brains is to compute the current state of the world, compare it to the desired state of the world and select motor programs that drive behavior minimizing any mismatch. The circuits underlying these functions are the key to understand brains in general, but so far they are completely unknown. Three problems have hindered progress: 1) The animal’s desired state of the world is rarely known. 2) Most studies in simple models have focused on sensory driven, reflex-like processes, and not considered self-initiated behavior. 3) The circuits underlying complex behaviors in vertebrates are widely distributed, containing millions of neurons. With this proposal I aim at overcoming these problems using insects, whose tiny brains solve the same basic problems as our brains but with 100,000 times fewer cells. Moreover, the central complex, a single conserved brain region consisting of only a few thousand neurons, is crucial for sensory integration, motor control and state-dependent modulation, essentially being a ‘brain in the brain’. To simplify the problem further I will focus on navigation behavior. Here, the desired and actual states of the world are equal to the desired and current headings of the animal, with mismatches resulting in compensatory steering. I have previously shown how the central complex encodes the animal’s current heading. Now I will use behavioral training to generate animals with highly defined desired headings, and correlate neural activity with the animal’s ‘intentions’ and actions - at the level of identified neurons. To establish the involved conserved core circuitry valid across insects I will compare species with distinct lifestyles. Secondly, I will reveal how these circuits have evolved to account for each species’ unique ecology. The proposed work will provide a coherent framework to study key concepts of fundamental brain functions in unprecedented detail - using a single, conserved, but flexible neural circuit.

Max ERC Funding

1 500 000 €

Duration

Start date: 2017-01-01, End date: 2021-12-31

Project acronymCAPTURE

ProjectCApturing Paradata for documenTing data creation and Use for the REsearch of the future

Researcher (PI)Isto HUVILA

Host Institution (HI)UPPSALA UNIVERSITET

Call DetailsConsolidator Grant (CoG), SH3, ERC-2018-COG

Summary"Considerable investments have been made in Europe and worldwide in research data infrastructures. Instead of a general lack of data about data, it has become apparent that the pivotal factor that drastically constrains the use of data is the absence of contextual knowledge about how data was created and how it has been used. This applies especially to many branches of SSH research where data is highly heterogeneous, both by its kind (e.g. being qualitative, quantitative, naturalistic, purposefully created) and origins (e.g. being historical/contemporary, from different contexts and geographical places). The problem is that there may be enough metadata (data about data) but there is too little paradata (data on the processes of its creation and use).
In contrast to the rather straightforward problem of describing the data, the high-risk/high-gain problem no-one has managed to solve, is the lack of comprehensive understanding of what information about the creation and use of research data is needed and how to capture enough of that information to make the data reusable and to avoid the risk that currently collected vast amounts of research data become useless in the future. The wickedness of the problem lies in the practical impossibility to document and keep everything and the difficulty to determine optimal procedures for capturing just enough.
With an empirical focus on archaeological and cultural heritage data, which stands out by its extreme heterogeneity and rapid accumulation due to the scale of ongoing development-led archaeological fieldwork, CAPTURE develops an in-depth understanding of how paradata is #1 created and #2 used at the moment, #3 elicits methods for capturing paradata on the basis of the findings of #1-2, #4 tests the new methods in field trials, and #5 synthesises the findings in a reference model to inform the capturing of paradata and enabling data-intensive research using heterogeneous research data stemming from diverse origins.
"

"Considerable investments have been made in Europe and worldwide in research data infrastructures. Instead of a general lack of data about data, it has become apparent that the pivotal factor that drastically constrains the use of data is the absence of contextual knowledge about how data was created and how it has been used. This applies especially to many branches of SSH research where data is highly heterogeneous, both by its kind (e.g. being qualitative, quantitative, naturalistic, purposefully created) and origins (e.g. being historical/contemporary, from different contexts and geographical places). The problem is that there may be enough metadata (data about data) but there is too little paradata (data on the processes of its creation and use).
In contrast to the rather straightforward problem of describing the data, the high-risk/high-gain problem no-one has managed to solve, is the lack of comprehensive understanding of what information about the creation and use of research data is needed and how to capture enough of that information to make the data reusable and to avoid the risk that currently collected vast amounts of research data become useless in the future. The wickedness of the problem lies in the practical impossibility to document and keep everything and the difficulty to determine optimal procedures for capturing just enough.
With an empirical focus on archaeological and cultural heritage data, which stands out by its extreme heterogeneity and rapid accumulation due to the scale of ongoing development-led archaeological fieldwork, CAPTURE develops an in-depth understanding of how paradata is #1 created and #2 used at the moment, #3 elicits methods for capturing paradata on the basis of the findings of #1-2, #4 tests the new methods in field trials, and #5 synthesises the findings in a reference model to inform the capturing of paradata and enabling data-intensive research using heterogeneous research data stemming from diverse origins.
"

SummaryThe mammalian brain is assembled from thousands of neuronal cell types that are organized into distinct circuits to perform behaviourally relevant computations. To gain mechanistic insights about brain function and to treat specific diseases of the nervous system it is crucial to understand what these local circuits are computing and how they achieve these computations. By examining the structure and function of a few genetically identified and experimentally accessible neural circuits we plan to address fundamental questions about the functional architecture of neural circuits. First, are cell types assigned to a unique functional circuit with a well-defined function or do they participate in multiple circuits (multitasking cell types), adjusting their role depending on the state of these circuits? Second, does a neural circuit perform a single computation or depending on the information content of its inputs can it carry out radically different functions? Third, how, among the large number of other cell types, do the cells belonging to the same functional circuit connect together during development? We use the mouse retina as a model system to address these questions. Finally, we will study the structure and function of a specialised neural circuit in the human fovea that enables humans to read. We predict that our insights into the mechanism of multitasking, network switches and the development of selective connectivity will be instructive to study similar phenomena in other brain circuits. Knowledge of the structure and function of the human fovea will open up new opportunities to correlate human retinal function with human visual behaviour and our genetic technologies to study human foveal function will allow us and others to design better strategies for restoring vision for the blind.

The mammalian brain is assembled from thousands of neuronal cell types that are organized into distinct circuits to perform behaviourally relevant computations. To gain mechanistic insights about brain function and to treat specific diseases of the nervous system it is crucial to understand what these local circuits are computing and how they achieve these computations. By examining the structure and function of a few genetically identified and experimentally accessible neural circuits we plan to address fundamental questions about the functional architecture of neural circuits. First, are cell types assigned to a unique functional circuit with a well-defined function or do they participate in multiple circuits (multitasking cell types), adjusting their role depending on the state of these circuits? Second, does a neural circuit perform a single computation or depending on the information content of its inputs can it carry out radically different functions? Third, how, among the large number of other cell types, do the cells belonging to the same functional circuit connect together during development? We use the mouse retina as a model system to address these questions. Finally, we will study the structure and function of a specialised neural circuit in the human fovea that enables humans to read. We predict that our insights into the mechanism of multitasking, network switches and the development of selective connectivity will be instructive to study similar phenomena in other brain circuits. Knowledge of the structure and function of the human fovea will open up new opportunities to correlate human retinal function with human visual behaviour and our genetic technologies to study human foveal function will allow us and others to design better strategies for restoring vision for the blind.

SummaryThe cerebellum is a critical regulator of motor function, which acts to integrate ongoing body states, sensory inputs and desired outcomes to adjust motor output. This motor control is achieved by a relatively small number of neuron types receiving two main sources of inputs and forming a single output pathway, the axons of Purkinje cells. Although the cerebellum is one of the first structures of the brain to differentiate, it undergoes a prolonged differentiation period such that mature cellular and circuit configuration is achieved only late after birth. Despite the functional importance of this structure, the molecular mechanisms that control type-specific cerebellar neurons generation, differentiation, and circuit assembly are poorly understood and are the topic of the present study.
In my research program, I propose to investigate the transcriptional programs that control the generation of distinct subtypes of cerebellar neurons from progenitors, including Purkinje cells, granule cells and molecular layer interneurons (Work Package 1); the diversity of Purkinje cells across cerebellar regions (Work Package 2) and the postnatal differentiation and circuit integration of granule cells and molecular layer interneurons (Work Package 3). The general bases of the approach I propose consist in: i) specifically label cerebellar neuron progenitors and their progeny at sequential developmental time points pre- and post-natally using birthdate-based tagging, ii) FAC-sort these distinct cell types, iii) isolate these cells and identify their transcriptional signatures with single-cell resolution, iv) functionally interrogate top candidate genes and associated transcriptional programs using in vivo gain- and loss-of-function approaches. Together, these experiments aim at deciphering the cell-intrinsic processes controlling cerebellar circuit formation, towards a better understanding of the molecular mechanisms underlying cerebellar function and dysfunction.

The cerebellum is a critical regulator of motor function, which acts to integrate ongoing body states, sensory inputs and desired outcomes to adjust motor output. This motor control is achieved by a relatively small number of neuron types receiving two main sources of inputs and forming a single output pathway, the axons of Purkinje cells. Although the cerebellum is one of the first structures of the brain to differentiate, it undergoes a prolonged differentiation period such that mature cellular and circuit configuration is achieved only late after birth. Despite the functional importance of this structure, the molecular mechanisms that control type-specific cerebellar neurons generation, differentiation, and circuit assembly are poorly understood and are the topic of the present study.
In my research program, I propose to investigate the transcriptional programs that control the generation of distinct subtypes of cerebellar neurons from progenitors, including Purkinje cells, granule cells and molecular layer interneurons (Work Package 1); the diversity of Purkinje cells across cerebellar regions (Work Package 2) and the postnatal differentiation and circuit integration of granule cells and molecular layer interneurons (Work Package 3). The general bases of the approach I propose consist in: i) specifically label cerebellar neuron progenitors and their progeny at sequential developmental time points pre- and post-natally using birthdate-based tagging, ii) FAC-sort these distinct cell types, iii) isolate these cells and identify their transcriptional signatures with single-cell resolution, iv) functionally interrogate top candidate genes and associated transcriptional programs using in vivo gain- and loss-of-function approaches. Together, these experiments aim at deciphering the cell-intrinsic processes controlling cerebellar circuit formation, towards a better understanding of the molecular mechanisms underlying cerebellar function and dysfunction.